Target Orientation Estimation Using Depth Sensing

1. A computer based method for estimating a real-time orientation measure for a target using depth video image data, the method comprising the steps of: receiving as input a feed of depth video frames, the depth video frames capturing the target in real-time and including depth pixel data; storing a target-specific training set of depth video frames during an initial training period; identifying frames of the target-specific training set of depth video frames capturing the target in a dominant orientation by analyzing the target-specific training set of depth video frames; comparing, based in appearance, a current depth video frame with the target-specific training set of depth video frames to determine whether the current depth video frame includes the target in the dominant orientation; determining a current orientation measure based in part on depth data corresponding to the current depth video frame, the current orientation measure corresponding to a current orientation of the target captured in the current depth video frame; and setting the current orientation measure to a reference orientation measure in response to determining that the current depth video frame includes the target in the dominant orientation.

2. The method of claim 1 , wherein analyzing the target-specific training set of depth video frames includes performing a Parzen-window based PDF and determining the mode of the PDF.

3. The method of claim 1 , wherein analyzing the target-specific training set of depth video frames includes segmenting each depth video frame to determine a segment of the frame that includes image data corresponding to the target.

4. The method of claim 3 , wherein analyzing the target-specific training set of depth video frames further includes tracking the segment position from frame to frame based on one of an elliptic fitting method or a Mean shift algorithm.

5. The method of claim 1 , wherein comparing, based in appearance, further comprises: determining a projection matrix and a pose curve using PCA analysis of the target-specific training set of depth video frames; projecting the current depth video frame onto a set of multidimensional eigenspaces based on the projection matrix; and estimating an orientation based on the projected current depth video frame.

6. The method of claim 5 , wherein estimating the orientation includes finding a nearest neighbor based on the projection of the current depth video frame and points of the pose curve.

7. The method of claim 5 , wherein estimating the orientation includes performing a linear interpolation between points in the pose curve corresponding to the projected current depth video frame.

8. The method of claim 1 , wherein determining the current orientation measure further comprises: estimating an optical flow between feature points in a previous depth video frame and the current depth video frame to determine two-dimensional motion fields; recovering three-dimensional rotation and translation parameters between the previous depth video frame and the current depth video frame using the two-dimensional motion fields and depth constrains based on the depth pixel data corresponding to the feature points; and setting the current orientation measure to an accumulated orientation value based on an orientation measure for the previous depth video frame and the three-dimensional rotation and translation parameters.

9. The method of claim 1 , wherein the target is a driver's head and the orientation is a head pose.

10. A computer readable storage medium for estimating a real-time orientation measure for a target using depth video image data, comprising a computer program that when executed by a computer processor implements the steps of: receiving as input a feed of depth video frames, the depth video frames capturing the target in real-time and including depth pixel data; storing a target-specific training set of depth video frames during an initial training period; identifying frames of the target-specific training set of depth video frames capturing the target in a dominant orientation by analyzing the target-specific training set of depth video frames; comparing, based in appearance, a current depth video frame with the target-specific training set of depth video frames to determine whether the current depth video frame includes the target in the dominant orientation; determining a current orientation measure based in part on depth data corresponding to the current depth video frame, the current orientation measure corresponding to a current orientation of the target captured in the current depth video frame; and setting the current orientation measure to a reference orientation measure in response to determining that the current depth video frame includes the target in the dominant orientation.

11. A system for estimating a real-time orientation measure for a target using depth video image data, the system comprising: means for receiving as input a feed of depth video frames, the depth video frames capturing the target in real-time and including depth pixel data; means for storing a target-specific training set of depth video frames during an initial training period; means for identifying frames of the target-specific training set of depth video frames capturing the target in a dominant orientation by analyzing the target-specific training set of depth video frames; means for comparing, based in appearance, a current depth video frame with the target-specific training set of depth video frames to determine whether the current depth video frame includes the target in the dominant orientation; means for determining a current orientation measure based in part on depth data corresponding to the current depth video frame, the current orientation measure corresponding to a current orientation of the target captured in the current depth video frame; and means for setting the current orientation measure to a reference orientation measure in response to determining that the current depth video frame includes the target in the dominant orientation.